Embed Size (px)
Citation preview
Package ‘bnclassify’March 12, 2020
Title Learning Discrete Bayesian Network Classifiers from Data
DescriptionState-of-the art algorithms for learning discrete Bayesian network classifiers from data, includ-ing a number of those described in Bielza & Larranaga (2014) <doi:10.1145/2576868>, with func-tions for prediction, model evaluation and inspection.
Version 0.4.5
URL http://github.com/bmihaljevic/bnclassify
BugReports http://github.com/bmihaljevic/bnclassify/issues
Depends R (>= 3.2.0)
Imports assertthat (>= 0.1), entropy(>= 1.2.0), matrixStats(>=0.14.0), rpart(>= 4.1-8), Rcpp,
Suggests graph(>= 1.42.0), gRain(>= 1.2-3), gRbase(>= 1.7-0.1), mlr(>=2.2), testthat(>= 0.8.1), knitr(>= 1.10.5), ParamHelpers(>=1.5), Rgraphviz(>= 2.8.1), rmarkdown(>= 0.7), mlbench, covr
License GPL (>= 2)
Maintainer Mihaljevic Bojan <[email protected]>
VignetteBuilder knitr
RoxygenNote 7.0.2
LinkingTo Rcpp, BH
SystemRequirements C++11
NeedsCompilation yes
Author Mihaljevic Bojan [aut, cre],Bielza Concha [aut],Larranaga Pedro [aut],Wickham Hadley [ctb] (some code extracted from memoise package)
Repository CRAN
Date/Publication 2020-03-12 17:40:02 UTC
1
2 accuracy
R topics documented:accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2aode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3as_mlr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3bnc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4bnclassify . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5bnc_bn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6bnc_dag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7car . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7cmi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8cv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8grain_and_graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9greedy_wrapper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10inspect_bnc_bn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11inspect_bnc_dag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12learn_params . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13loglik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15nb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16plot.bnc_dag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17predict.bnc_fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18tan_chowliu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19voting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Index 21
accuracy Compute predictive accuracy.
Description
Compute predictive accuracy.
Usage
accuracy(x, y)
Arguments
x A vector of predicted labels.y A vector of true labels.
Examples
data(car)nb <- bnc('nb', 'class', car, smooth = 1)p <- predict(nb, car)accuracy(p, car$class)
aode 3
aode Learn an AODE ensemble.
Description
If there is a single predictor then returns a naive Bayes.
Usage
aode(class, dataset, features = NULL)
Arguments
class A character. Name of the class variable.dataset The data frame from which to learn the classifier.features A character vector. The names of the features. This argument is ignored if
dataset is provided.
Value
A bnc_aode or a bnc_dag (if returning a naive Bayes)
as_mlr Convert to mlr.
Description
Convert a bnc_bn to a Learner object.
Usage
as_mlr(x, dag, id = "1")
Arguments
x A bnc_bn object.dag A logical. Whether to learn structure on each training subsample. Parameters
are always learned.id A character.
Examples
data(car)nb <- bnc('nb', 'class', car, smooth = 1)## Not run: library(mlr)## Not run: nb_mlr <- as_mlr(nb, dag = FALSE, id = "ode_cl_aic")## Not run: nb_mlr
4 bnc
bnc Learn network structure and parameters.
Description
A convenience function to learn the structure and parameters in a single call. Must provide thename of the structure learning algorithm function; see bnclassify for the list.
Usage
bnc(dag_learner,class,dataset,smooth,dag_args = NULL,awnb_trees = NULL,awnb_bootstrap = NULL,manb_prior = NULL,wanbia = NULL
)
Arguments
dag_learner A character. Name of the structure learning function.
class A character. Name of the class variable.
dataset The data frame from which to learn network structure and parameters.
smooth A numeric. The smoothing value (α) for Bayesian parameter estimation. Non-negative.
dag_args A list. Optional additional arguments to dag_learner.
awnb_trees An integer. The number (M ) of bootstrap samples to generate.
awnb_bootstrap A numeric. The size of the bootstrap subsample, relative to the size of dataset(given in [0,1]).
manb_prior A numeric. The prior probability for an arc between the class and any feature.
wanbia A logical. If TRUE, WANBIA feature weighting is performed.
Examples
data(car)nb <- bnc('nb', 'class', car, smooth = 1)nb_manb <- bnc('nb', 'class', car, smooth = 1, manb_prior = 0.3)ode_cl_aic <- bnc('tan_cl', 'class', car, smooth = 1, dag_args = list(score = 'aic'))
bnclassify 5
bnclassify Learn discrete Bayesian network classifiers from data.
Description
State-of-the-art algorithms for learning discrete Bayesian network classifiers from data, with func-tions prediction, model evaluation and inspection.
Details
The learn more about the package, start with the vignettes: browseVignettes(package = "bnclassify").The following is a list of available functionalities:
Structure learning algorithms:
• nb: Naive Bayes (Minsky, 1961)
• tan_cl: Chow-Liu’s algorithm for one-dependence estimators (CL-ODE) (Friedman et al.,1997)
• fssj: Forward sequential selection and joining (FSSJ) (Pazzani, 1996)
• bsej: Backward sequential elimination and joining (BSEJ) (Pazzani, 1996)
• tan_hc: Hill-climbing tree augmented naive Bayes (TAN-HC) (Keogh and Pazzani, 2002)
• tan_hcsp: Hill-climbing super-parent tree augmented naive Bayes (TAN-HCSP) (Keogh andPazzani, 2002)
• aode: Averaged one-dependence estimators (AODE) (Webb et al., 2005)
Parameter learning methods (lp):
• Bayesian and maximum likelihood estimation
• Weighting attributes to alleviate naive bayes’ independence assumption (WANBIA) (Zaidi etal., 2013)
• Attribute-weighted naive Bayes (AWNB) (Hall, 2007)
• Model averaged naive Bayes (MANB) (Dash and Cooper, 2002)
Model evaluating:
• cv: Cross-validated estimate of accuracy
• logLik: Log-likelihood
• AIC: Akaike’s information criterion (AIC)
• BIC: Bayesian information criterion (BIC)
Predicting:
• predict: Inference for complete and/or incomplete data (the latter through gRain)
Inspecting models:
6 bnc_bn
• plot: Structure plotting (through Rgraphviz)
• print: Summary
• params: Access conditional probability tables
• nparams: Number of free parameters
• and more. See inspect_bnc_dag and inspect_bnc_bn.
References
Bielza C and Larranaga P (2014), Discrete Bayesian network classifiers: A survey. ACM ComputingSurveys, 47(1), Article 5.
Dash D and Cooper GF (2002). Exact model averaging with naive Bayesian classifiers. 19th Inter-national Conference on Machine Learning (ICML-2002), 91-98.
Friedman N, Geiger D and Goldszmidt M (1997). Bayesian network classifiers. Machine Learning,29, pp. 131–163.
Zaidi NA, Cerquides J, Carman MJ, and Webb GI (2013) Alleviating naive Bayes attribute in-dependence assumption by attribute weighting. Journal of Machine Learning Research, 14 pp.1947–1988.
GI. Webb, JR Boughton, and Z Wang (2005) Not so naive bayes: Aggregating one-dependenceestimators. Machine Learning, 58(1) pp. 5–24.
Hall M (2007). A decision tree-based attribute weighting filter for naive Bayes. Knowledge-BasedSystems, 20(2), pp. 120-126.
Koegh E and Pazzani M (2002).Learning the structure of augmented Bayesian classifiers. In Inter-national Journal on Artificial Intelligence Tools, 11(4), pp. 587-601.
Koller D, Friedman N (2009). Probabilistic Graphical Models: Principles and Techniques. MITPress.
Pazzani M (1996). Constructive induction of Cartesian product attributes. In Proceedings of theInformation, Statistics and Induction in Science Conference (ISIS-1996), pp. 66-77
bnc_bn Bayesian network classifier with structure and parameters.
Description
A Bayesian network classifier with structure and parameters. Returned by lp and bnc functions.You can use it to classify data (with predict). Can estimate its predictive accuracy with cv, plotits structure (with plot), print a summary to console (print), inspect it with functions documentedin inspect_bnc_bn and inspect_bnc_dag, and convert it to mlr, grain, and graph objects –seeas_mlr and grain_and_graph.
bnc_dag 7
Examples
data(car)tan <- bnc('tan_cl', 'class', car, smooth = 1)tanp <- predict(tan, car)head(p)## Not run: plot(tan)nparams(tan)
bnc_dag Bayesian network classifier structure.
Description
A Bayesian network classifier structure, returned by functions such as nb and tan_cl. You can plotits structure (with plot), print a summary to console (print), inspect it with functions documentedin inspect_bnc_dag, and convert it to a graph object with grain_and_graph.
Examples
data(car)nb <- tan_cl('class', car)nb## Not run: plot(nb)narcs(nb)
car Car Evaluation Data Set.
Description
Data set from the UCI repository: https://archive.ics.uci.edu/ml/datasets/Car+Evaluation.
Format
A data.frame with 7 columns and 1728 rows.
Source
http://goo.gl/GTXrCz
8 cv
cmi Compute the (conditional) mutual information between two variables.
Description
Computes the (conditional) mutual information between two variables. If z is not NULL then returnsthe conditional mutual information, I(X;Y |Z). Otherwise, returns mutual information, I(X;Y ).
Usage
cmi(x, y, dataset, z = NULL, unit = "log")
Arguments
x A length one character.
y A length one character.
dataset A data frame. Must contain x, y and, optionally, z columns.
z A character vector.
unit A character. Logarithm base. See entropy package.
Details
I(X;Y |Z) = H(X|Z) +H(Y |Z)−H(X,Y, Z)−H(Z), where H() is Shannon’s entropy.
Examples
data(car)cmi('maint', 'class', car)
cv Estimate predictive accuracy with stratified cross validation.
Description
Estimate predictive accuracy of a classifier with stratified cross validation. It learns the modelsfrom the training subsamples by repeating the learning procedures used to obtain x. It can keep thenetwork structure fixed and re-learn only the parameters, or re-learn both structure and parameters.
Usage
cv(x, dataset, k, dag = TRUE, mean = TRUE)
grain_and_graph 9
Arguments
x List of bnc_bn or a single bnc_bn. The classifiers to evaluate.
dataset The data frame on which to evaluate the classifiers.
k An integer. The number of folds.
dag A logical. Whether to learn structure on each training subsample. Parametersare always learned.
mean A logical. Whether to return mean accuracy for each classifier or to return ak-row matrix with accuracies per fold.
Value
A numeric vector of same length as x, giving the predictive accuracy of each classifier. If mean =FALSE then a matrix with k rows and a column per each classifier in x.
Examples
data(car)nb <- bnc('nb', 'class', car, smooth = 1)# CV a single classifiercv(nb, car, k = 10)nb_manb <- bnc('nb', 'class', car, smooth = 1, manb_prior = 0.5)cv(list(nb=nb, manb=nb_manb), car, k = 10)# Get accuracies on each foldcv(list(nb=nb, manb=nb_manb), car, k = 10, mean = FALSE)ode <- bnc('tan_cl', 'class', car, smooth = 1, dag_args = list(score = 'aic'))# keep structure fixed accross training subsamplescv(ode, car, k = 10, dag = FALSE)
grain_and_graph Convert to graph and gRain.
Description
Convert a bnc_dag to graphNEL and grain objects.
Usage
as_graphNEL(x)
as_grain(x)
Arguments
x The bnc_bn object. The Bayesian network classifier.
10 greedy_wrapper
Functions
• as_graphNEL: Convert to a graphNEL.
• as_grain: Convert to a grain.
Examples
data(car)nb <- bnc('nb', 'class', car, smooth = 1)# Requires the grain and graph packages installed## Not run: g <- as_grain(nb)## Not run: gRain::querygrain.grain(g)$buying
greedy_wrapper Learn Bayesian network classifiers in a a greedy wrapper fashion.
Description
Greedy wrapper algorithms for learning Bayesian network classifiers. All algorithms use cross-validated estimate of predictive accuracy to evaluate candidate structures.
Usage
fssj(class, dataset, k, epsilon = 0.01, smooth = 0, cache_reset = NULL)
bsej(class, dataset, k, epsilon = 0.01, smooth = 0, cache_reset = NULL)
tan_hc(class, dataset, k, epsilon = 0.01, smooth = 0, cache_reset = NULL)
kdb(class,dataset,k,kdbk = 2,epsilon = 0.01,smooth = 0,cache_reset = NULL
)
tan_hcsp(class, dataset, k, epsilon = 0.01, smooth = 0, cache_reset = NULL)
Arguments
class A character. Name of the class variable.
dataset The data frame from which to learn the classifier.
k An integer. The number of folds.
inspect_bnc_bn 11
epsilon A numeric. Minimum absolute improvement in accuracy required to keep search-ing.
smooth A numeric. The smoothing value (α) for Bayesian parameter estimation. Non-negative.
cache_reset A numeric. Number of iterations after which to reset the cache of conditionalprobability tables. A small number reduces the amount of memory used. NULLmeans the cache is never reset (the default).
kdbk An integer. The maximum number of feature parents per feature.
Value
A bnc_dag object.
References
Pazzani M (1996). Constructive induction of Cartesian product attributes. In Proceedings of theInformation, Statistics and Induction in Science Conference (ISIS-1996), pp. 66-77
Koegh E and Pazzani M (2002).Learning the structure of augmented Bayesian classifiers. In Inter-national Journal on Artificial Intelligence Tools, 11(4), pp. 587-601.
Examples
data(car)tanhc <- tan_hc('class', car, k = 5, epsilon = 0)## Not run: plot(tanhc)
inspect_bnc_bn Inspect a Bayesian network classifier (with structure and parameters).
Description
Functions for inspecting a bnc_bn object. In addition, you can query this object with the functionsdocumented in inspect_bnc_dag.
Usage
nparams(x)
manb_arc_posterior(x)
awnb_weights(x)
params(x)
values(x)
classes(x)
12 inspect_bnc_dag
Arguments
x The bnc_bn object. The Bayesian network classifier.
Functions
• nparams: Returns the number of free parameters in the model.
• manb_arc_posterior: Returns the posterior of each arc from the class according to theMANB method.
• awnb_weights: Returns the AWNB feature weights.
• params: Returns the list of CPTs, in the same order as vars.
• values: Returns the possible values of each variable, in the same order as vars.
• classes: Returns the possible values of the class variable.
Examples
data(car)nb <- bnc('nb', 'class', car, smooth = 1)nparams(nb)nb <- bnc('nb', 'class', car, smooth = 1, manb_prior = 0.5)manb_arc_posterior(nb)nb <- bnc('nb', 'class', car, smooth = 1, awnb_bootstrap = 0.5)awnb_weights(nb)
inspect_bnc_dag Inspect a Bayesian network classifier structure.
Description
Functions for inspecting a bnc_dag object.
Usage
class_var(x)
features(x)
vars(x)
families(x)
modelstring(x)
feature_families(x)
narcs(x)
learn_params 13
is_semi_naive(x)
is_anb(x)
is_nb(x)
is_ode(x)
Arguments
x The bnc_dag object. The Bayesian network classifier structure.
Functions
• class_var: Returns the class variable.
• features: Returns the features.
• vars: Returns all variables (i.e., features + class).
• families: Returns the family of each variable.
• modelstring: Returns the model string of the network in bnlearn format (adding a space inbetween two families).
• feature_families: Returns the family of each feature.
• narcs: Returns the number of arcs.
• is_semi_naive: Returns TRUE if x is a semi-naive Bayes.
• is_anb: Returns TRUE if x is an augmented naive Bayes.
• is_nb: Returns TRUE if x is a naive Bayes.
• is_ode: Returns TRUE if x is a one-dependence estimator.
Examples
data(car)nb <- bnc('nb', 'class', car, smooth = 1)narcs(nb)is_ode(nb)
learn_params Learn the parameters of a Bayesian network structure.
Description
Learn parameters with maximum likelihood or Bayesian estimation, the weighting attributes to alle-viate naive bayes’ independence assumption (WANBIA), attribute weighted naive Bayes (AWNB),or the model averaged naive Bayes (MANB) methods. Returns a bnc_bn.
14 learn_params
Usage
lp(x,dataset,smooth,awnb_trees = NULL,awnb_bootstrap = NULL,manb_prior = NULL,wanbia = NULL
)
Arguments
x The bnc_dag object. The Bayesian network classifier structure.
dataset The data frame from which to learn network parameters.
smooth A numeric. The smoothing value (α) for Bayesian parameter estimation. Non-negative.
awnb_trees An integer. The number (M ) of bootstrap samples to generate.
awnb_bootstrap A numeric. The size of the bootstrap subsample, relative to the size of dataset(given in [0,1]).
manb_prior A numeric. The prior probability for an arc between the class and any feature.
wanbia A logical. If TRUE, WANBIA feature weighting is performed.
Details
lp learns the parameters of each local distribution θijk = P (Xi = k | Pa(Xi) = j) as
θijk =Nijk + α
Nij· + riα,
where Nijk is the number of instances in dataset in which Xi = k and Pa(Xi) = j, Nij· =∑rik=1Nijk, ri is the cardinality of Xi, and all hyperparameters of the Dirichlet prior equal to
α. α = 0 corresponds to maximum likelihood estimation. Returns a uniform distribution whenNij· + riα = 0. With partially observed data, the above amounts to available case analysis.
WANBIA learns a unique exponent ’weight’ per feature. They are computed by optimizing condi-tional log-likelihood, and are bounded with all wi ∈ [0, 1]. For WANBIA estimates, set wanbia toTRUE.
In order to get the AWNB parameter estimate, provide either the awnb_bootstrap and/or theawnb_trees argument. The estimate is:
θAWNBijk =
θwi
ijk∑rik=1 θ
wi
ijk
,
while the weights wi are computed as
wi =1
M
M∑t=1
√1
dti,
loglik 15
where M is the number of bootstrap samples from dataset and dti the minimum testing depth ofXi in an unpruned classification tree learned from the t-th subsample (dti = 0 ifXi is omitted fromt-th tree).
The MANB parameters correspond to Bayesian model averaging over the naive Bayes models ob-tained from all 2n subsets over the n features. To get MANB parameters, provide the manb_priorargument.
Value
A bnc_bn object.
References
Hall M (2004). A decision tree-based attribute weighting filter for naive Bayes. Knowledge-basedSystems, 20(2), 120-126.
Dash D and Cooper GF (2002). Exact model averaging with naive Bayesian classifiers. 19th Inter-national Conference on Machine Learning (ICML-2002), 91-98.
Pigott T D (2001) A review of methods for missing data. Educational research and evaluation,7(4), 353-383.
Examples
data(car)nb <- nb('class', car)# Maximum likelihood estimationmle <- lp(nb, car, smooth = 0)# Bayesian estimaionbayes <- lp(nb, car, smooth = 0.5)# MANBmanb <- lp(nb, car, smooth = 0.5, manb_prior = 0.5)# AWNBawnb <- lp(nb, car, smooth = 0.5, awnb_trees = 10)
loglik Compute (penalized) log-likelihood.
Description
Compute (penalized) log-likelihood and conditional log-likelihood score of a bnc_bn object on adata set. Requires a data frame argument in addition to object.
Usage
## S3 method for class 'bnc_bn'AIC(object, ...)
## S3 method for class 'bnc_bn'
16 nb
BIC(object, ...)
## S3 method for class 'bnc_bn'logLik(object, ...)
cLogLik(object, ...)
Arguments
object A bnc_bn object.
... A data frame (D).
Details
log-likelihood = logP (D | θ),Akaike’s information criterion (AIC) = logP (D | θ)− 1
2 |θ|,
The Bayesian information criterion (BIC) score: = logP (D | θ)− logN2 |θ|,
where |θ| is the number of free parameters in object, D is the data set and N is the number ofinstances in D.
cLogLik computes the conditional log-likelihood of the model.
Examples
data(car)nb <- bnc('nb', 'class', car, smooth = 1)logLik(nb, car)AIC(nb, car)BIC(nb, car)cLogLik(nb, car)
nb Learn a naive Bayes network structure.
Description
Learn a naive Bayes network structure.
Usage
nb(class, dataset = NULL, features = NULL)
Arguments
class A character. Name of the class variable.
dataset The data frame from which to learn the classifier.
features A character vector. The names of the features. This argument is ignored ifdataset is provided.
plot.bnc_dag 17
Value
A bnc_dag object.
Examples
data(car)nb <- nb('class', car)nb2 <- nb('class', features = letters[1:10])## Not run: plot(nb2)
plot.bnc_dag Plot the structure.
Description
If node labels are to small to be viewed properly, you may fix label fontsize with argument fontsize.Also, you may try multiple different layouts.
Usage
## S3 method for class 'bnc_dag'plot(x, y, layoutType = "dot", fontsize = NULL, ...)
Arguments
x The bnc_dag object. The Bayesian network classifier structure.
y Not used
layoutType a character. Optional.
fontsize integer Font size for node labels. Optional.
... Not used.
Examples
# Requires the graph and Rgraphviz packages to be installed.data(car)nb <- nb('class', car)nb <- nb('class', car)## Not run: plot(nb)## Not run: plot(nb, fontsize = 20)## Not run: plot(nb, layoutType = 'circo')## Not run: plot(nb, layoutType = 'fdp')## Not run: plot(nb, layoutType = 'osage')## Not run: plot(nb, layoutType = 'twopi')## Not run: plot(nb, layoutType = 'neato')
18 predict.bnc_fit
predict.bnc_fit Predicts class labels or class posterior probability distributions.
Description
Predicts class labels or class posterior probability distributions.
Usage
## S3 method for class 'bnc_fit'predict(object, newdata, prob = FALSE, ...)
Arguments
object A bnc_bn object.
newdata A data frame containing observations whose class has to be predicted.
prob A logical. Whether class posterior probability should be returned.
... Ignored.
Details
Ties are resolved randomly. Inference is much slower if newdata contains NAs.
Value
If prob=FALSE, then returns a length-N factor with the same levels as the class variable in x, whereN is the number of rows in newdata. Each element is the most likely class for the correspondingrow in newdata. If prob=TRUE, returns aN byC numeric matrix, whereC is the number of classes;each row corresponds to the class posterior of the instance.
Examples
data(car)nb <- bnc('nb', 'class', car, smooth = 1)p <- predict(nb, car)head(p)p <- predict(nb, car, prob = TRUE)head(p)
tan_chowliu 19
tan_chowliu Learns a one-dependence estimator using Chow-Liu’s algorithm.
Description
Learns a one-dependence Bayesian classifier using Chow-Liu’s algorithm, by maximizing eitherlog-likelihood, the AIC or BIC scores; maximizing log-likelihood corresponds to the well-knowntree augmented naive Bayes (Friedman et al., 1997). When maximizing AIC or BIC the outputmight be a forest-augmented rather than a tree-augmented naive Bayes.
Usage
tan_cl(class, dataset, score = "loglik", root = NULL)
Arguments
class A character. Name of the class variable.
dataset The data frame from which to learn the classifier.
score A character. The score to be maximized. 'loglik', 'bic', and 'aic' returnthe maximum likelihood, maximum BIC and maximum AIC tree/forest, respec-tively.
root A character. The feature to be used as root of the augmenting tree. Only onefeature can be supplied, even in case of an augmenting forest. This argument isoptional.
Value
A bnc_dag object.
References
Friedman N, Geiger D and Goldszmidt M (1997). Bayesian network classifiers. Machine Learning,29, pp. 131–163.
Examples
data(car)ll <- tan_cl('class', car, score = 'loglik')## Not run: plot(ll)ll <- tan_cl('class', car, score = 'loglik', root = 'maint')## Not run: plot(ll)aic <- tan_cl('class', car, score = 'aic')bic <- tan_cl('class', car, score = 'bic')
20 voting
voting Congress Voting Data Set.
Description
Data set from the UCI repository https://archive.ics.uci.edu/ml/datasets/Congressional+Voting+Records.
Format
A data.frame with 17 columns and 435 rows.
Source
http://goo.gl/GTXrCz
Index
accuracy, 2AIC, 5AIC.bnc_bn (loglik), 15aode, 3, 5as_grain (grain_and_graph), 9as_graphNEL (grain_and_graph), 9as_mlr, 3, 6awnb_weights (inspect_bnc_bn), 11
BIC, 5BIC.bnc_bn (loglik), 15bnc, 4, 6bnc_bn, 3, 6, 9, 11–13, 15, 16, 18bnc_dag, 7, 9, 11–14, 17, 19bnclassify, 4, 5bsej, 5bsej (greedy_wrapper), 10
car, 7class_var (inspect_bnc_dag), 12classes (inspect_bnc_bn), 11cLogLik (loglik), 15cmi, 8cv, 5, 6, 8
families (inspect_bnc_dag), 12feature_families (inspect_bnc_dag), 12features (inspect_bnc_dag), 12fssj, 5fssj (greedy_wrapper), 10
grain, 9grain_and_graph, 6, 7, 9greedy_wrapper, 10
inspect_bnc_bn, 6, 11inspect_bnc_dag, 6, 7, 11, 12is_anb (inspect_bnc_dag), 12is_nb (inspect_bnc_dag), 12is_ode (inspect_bnc_dag), 12is_semi_naive (inspect_bnc_dag), 12
kdb (greedy_wrapper), 10
learn_params, 13Learner, 3logLik, 5loglik, 15logLik.bnc_bn (loglik), 15lp, 5, 6lp (learn_params), 13
manb_arc_posterior (inspect_bnc_bn), 11modelstring (inspect_bnc_dag), 12
narcs (inspect_bnc_dag), 12nb, 5, 7, 16nparams, 6nparams (inspect_bnc_bn), 11
params, 6params (inspect_bnc_bn), 11plot, 6, 7plot.bnc_dag, 17predict, 5, 6predict.bnc_fit, 18print, 6, 7
tan_chowliu, 19tan_cl, 5, 7tan_cl (tan_chowliu), 19tan_hc, 5tan_hc (greedy_wrapper), 10tan_hcsp, 5tan_hcsp (greedy_wrapper), 10
values (inspect_bnc_bn), 11vars, 12vars (inspect_bnc_dag), 12voting, 20
21
